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A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.

Marsolo K, Margolis PA, Forrest CB, Colletti RB, Hutton JJ - EGEMS (Wash DC) (2015)

Bottom Line: Additional standards are needed in order for this vision to be achieved, however.We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build.We have also highlighted opportunities where sponsors could help accelerate progress.

View Article: PubMed Central - PubMed

Affiliation: Cincinnati Children's Hospital Medical Center.

ABSTRACT

Introduction: We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research.

Description of architecture: We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests.

Suggestions for future use: The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however.

Conclusions: We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.

No MeSH data available.


Longitudinal Version of the Pre-Visit Planning ReportsNote: Clinicians can view multiple measures of a patient’s status over time, as well as previous treatments. This information can be helpful when determining a new treatment plan if a patient is not responding to the current treatment.
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f6-egems1168: Longitudinal Version of the Pre-Visit Planning ReportsNote: Clinicians can view multiple measures of a patient’s status over time, as well as previous treatments. This information can be helpful when determining a new treatment plan if a patient is not responding to the current treatment.

Mentions: Soon after deploying the care management reports, we received feedback from users about usability issues, such as cumbersome workflows when trying to access multiple reports (many centers do pre-visit planning once a week and generate reports for all patients scheduled in the next week) and formatting issues when trying to print them. Working with a group of power users, along with faculty and graduate students with experience in interaction design and information visualization, we initiated a redesign of the care management reports. We interviewed participants to determine how they interacted with the reports as well as the information they needed to see in each section. This led to the creation of several design prototypes that were shared with the network. After gathering feedback, the most popular prototypes were translated into production-level reports. These new reports, shown in Figures 5 and 6, were deployed along with new methods of access that greatly enhanced user efficiency.


A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.

Marsolo K, Margolis PA, Forrest CB, Colletti RB, Hutton JJ - EGEMS (Wash DC) (2015)

Longitudinal Version of the Pre-Visit Planning ReportsNote: Clinicians can view multiple measures of a patient’s status over time, as well as previous treatments. This information can be helpful when determining a new treatment plan if a patient is not responding to the current treatment.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4562738&req=5

f6-egems1168: Longitudinal Version of the Pre-Visit Planning ReportsNote: Clinicians can view multiple measures of a patient’s status over time, as well as previous treatments. This information can be helpful when determining a new treatment plan if a patient is not responding to the current treatment.
Mentions: Soon after deploying the care management reports, we received feedback from users about usability issues, such as cumbersome workflows when trying to access multiple reports (many centers do pre-visit planning once a week and generate reports for all patients scheduled in the next week) and formatting issues when trying to print them. Working with a group of power users, along with faculty and graduate students with experience in interaction design and information visualization, we initiated a redesign of the care management reports. We interviewed participants to determine how they interacted with the reports as well as the information they needed to see in each section. This led to the creation of several design prototypes that were shared with the network. After gathering feedback, the most popular prototypes were translated into production-level reports. These new reports, shown in Figures 5 and 6, were deployed along with new methods of access that greatly enhanced user efficiency.

Bottom Line: Additional standards are needed in order for this vision to be achieved, however.We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build.We have also highlighted opportunities where sponsors could help accelerate progress.

View Article: PubMed Central - PubMed

Affiliation: Cincinnati Children's Hospital Medical Center.

ABSTRACT

Introduction: We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research.

Description of architecture: We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests.

Suggestions for future use: The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however.

Conclusions: We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.

No MeSH data available.